> Communication matters most when you're dealing with cross-org concerns and those that master it are usually the more friendly and pleasant ones.
I don't agree with the second one, but agree with the first.
Throughout my corporate career so far, I have found plenty of hot air/pretty picture slide decks that exist solely for ladder climbers to climb. Said ladder climbers are usually all smiles in public and "friendly", but you have to watch out for knives behind your back.
> Thanks for that! It is worth noting that taking advantage of the post-rotation distribution
I again feel this claim is too strong. Rotations have been used in information theory/wireless communications for decades at this point, with appropriate scaling done at channel inputs/outputs to hit channel capacity. The signals then pass through the appropriate codebooks that take advantage of the post-rotated+whitened signal.
Our cellphones today are powered by such technology.
I agree with your claim when restricted to deep learning. But I do not agree with the broad characterization that taking advantage of post-rotation distributions was only first done in your work.
Wow, yes - you are completely correct (read through the note in detail now).
Though, as your paper also notes, the quantizer values themselves aren't fundamentally novel to either paper.
Lloyd Max scalar quantizers have been studied for a very, very long time.
And the specific Lloyd Max values for the Gaussian input distribution have been obtained in many papers across signal processing and information theory.
For example, TurboQuant makes use of QJL (quantized Johnson Lindenstrauss transformations). One of the first papers to characterize the QJL and in fact the rate distortion tradeoff for quantized matrix multiplication in general is "Optimal Quantization for Matrix Multiplication" (https://arxiv.org/abs/2410.13780) by Ordentlich and Polyanskiy.
There is also a more accessible survey paper around quantized matrix multiplication called "High-Rate Quantized Matrix Multiplication: Theory and Practice" (https://arxiv.org/abs/2601.17187), by the same authors.
TurboQuant is known across the industry to not be state of the art. There are superior schemes for KV quant at every bitrate. Eg, SpectralQuant: https://github.com/Dynamis-Labs/spectralquant among many, many papers.
> Given that TurboQuant results in a 6x reduction in memory usage for KV caches
All depends on baseline. The "6x" is by stylistic comparison to a BF16 KV cache; not a state of the art 8 or 4 bit KV cache scheme.
The problem is, "scope" is often equated to "how many people worked in my empire" rather than "how much business value did my work X generate".
The two things are vastly different, and I have seen the distinction/oversimplification play out over and over in my own career as well as many others around me.
As an extreme on the "individual technical expert side", there are things out there that can pretty much only be accomplished with a few people around the world who possess the dedicated expertise. These results can't be replicated by a cobbled together team of 10 or 100 people even though the latter sounds more impressive for "scope".
Some organizations do a decent job of recognizing these different "archetypes", many don't.
>80%-90% or so of real life vectorization can be achieved in C or C++ just by writing code in a way that it can be autovectorized.
Yep. I was pleasantly surprised by the autovectorization quality with recent clang at work a few days ago. If you write code that the compiler can infer to be multiples of 4, 8, etc the compiler goes off and emits pretty decent NEON/AVX code. The rest as you say is handled quite well by intrinsics these days.
Autovectorization was definitely poorer 5-10 years ago on older compiler toolchains.
1 - Very few people conduct "proper scholarship", and fail to trace ideas back to their original inception and cite them correctly. This happens time and again in deep learning, where 30+ year old ideas are claimed as "novel" over and over. Many times out of malice by the authors, sometimes out of ignorance.
2 - Peer review in many parts of the industry+research is a joke. Mostly shouldered by early graduate students who don't really know the field well and an incredibly noisy process.
3 - It is common practice now to dump out one's "kitchen sink" of ideas rather than properly refined stuff. Hence the increase in LinkedIn spam, blog spam, arXiv spam style of papers.
> I don't think there are many (or any) upsides to the well documented downsides.
C++ template metaprogramming still remains extremely powerful. Projects like CUTLASS, etc could not be written to give best performance in as ergonomic a way in Rust.
There is a reason why the ML infra community mostly goes with Python-like DSL's, or template metaprogramming frameworks.
Last I checked there are no alternatives at scale for this.
As others have pointed out, these phenomena are well known to many folks across companies in the AI infra space. It doesn't really break new ground. This article is a good exposition of the basic strategies though.
What I would have loved is a discussion around collectives/multi-node setups. And showing how to get determinism at low performance penalty for multi-node reduction collectives.
> A page will be loaded in if any part of it is useful. Given that functions will be laid out more or less randomly throughout a shared library, and programs use a randomly scattered subset of the functions, I think its safe to say that you'll get a lot of bytes read in to ram that are never used.
Maybe Dropbox's is the best out of a bunch of mediocre syncing solutions. However, I do wish to add that Dropbox struggles with scale: lots of small files slow down sync to a crawl and spin CPU at 100%.
Concretely, I had a certain folder with 100,000+ small files for some research I was doing with a collaborator. It took my laptop 24 hours to sync this folder, and throughout my CPU was at 100% throughout. For comparison, rsync got the same job done in a few minutes. See https://help.dropbox.com/accounts-billing/space-storage/file... , the above terrible experience is in line with this caveat.
> that I don't see a future where they are unseated.
The way I see it, consumers (at least right now) are free not to buy into the hype and consume whatever it is that these companies produce. So if prices are too high or quality too low, a new company can enter in.
The real danger I see is the possibility that essentially all consumers will be forced to pay for this stuff through some regulatory capture, in a similar fashion to the state of the healthcare industry in the US.
I don't agree with the second one, but agree with the first.
Throughout my corporate career so far, I have found plenty of hot air/pretty picture slide decks that exist solely for ladder climbers to climb. Said ladder climbers are usually all smiles in public and "friendly", but you have to watch out for knives behind your back.